Can We Detect Fraud in the Blockchain Using Machine Learning? | by Noah Mukhtar | Jan, 2023

Can We Detect Fraud in the Blockchain Using Machine Learning? | by Noah Mukhtar | Jan, 2023

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Since the emergence of blockchain, it has never been more seamless for companies, banks, and customers to trade goods and transfer money. With this new era of e-commerce, the blockchain has acted as an attractive alternative that bypasses traditional intermediaries, and with that, we discover new ways to commit financial crimes, and with the vast collection of data we have today, we need to develop new ways to beat them.

Is Fraud Changing?

Bad actors are concealing their trail through one of the community’s most highly accredited tokens: Ethereum.

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Can Ethereum Be Exploited?

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Is There a Rise in Crime?

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Why Do We Need Data Science?

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The following steps explain the approach in data construction:

Problem: Imbalanced Dataset

Tradeoff: Recall vs. Precision

Solution:

Classification Models

Classification Model Scores

Feature Importance

Feature Importance of Classification Models

The results of the visualization revealed that the two features emerged as the most significant attributes in determining fraudulent transactions are:

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“Time Diff between first and last (Mins)” can be a good indication of fraud on the blockchain because it can help detect suspicious activities that occur within a short period of time. For example, if a large number of transactions are made within a very short time frame, it could indicate that the transactions are being made by a bot or an automated script rather than by a human.

Additionally, it can be a sign of a coordinated attack where multiple transactions are made simultaneously to flood the network with fake transactions.

“Unique received from addresses” can be a good indication of fraud on the blockchain because it can help detect suspicious activities that involve multiple addresses.

For example, if a single transaction is made from many different addresses, it could indicate that the transactions are being made by someone who is attempting to evade detection. It could also indicate a case of a group of individuals working together to commit fraud, or a possible money laundering operation.

Moreover, having multiple sources of funding in a transaction, or many different “from addresses” could also be a sign of a transaction that was made by an entity that may not have the proper authorization to make the transaction, or an entity attempting to anonymize its identity.

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